The University of St. Thomas has been recognized as a 2025 AWS Champions Award winner for its data modernization initiative that replaces siloed infrastructure with a unified, AI-ready data architecture. Leveraging a modern data mesh framework, the university is transforming how data is accessed, governed, and used to create a scalable foundation for integrations, business intelligence, automation, and AI innovation.

This initiative aligns directly with the university’s mission and strategic priorities, supporting both operational efficiency and the advancement of initiatives such as the Institute for AI for the Common Good.

Project Overview
Facing a growing demand for personalized services, real-time insights, and cross-system agility, the University of St. Thomas launched a transformational data strategy. The solution involved building an internal data lake on AWS, enhanced by a data mesh architecture that ensures high-quality, decentralized data access while eliminating costly integrations and reporting bottlenecks.

The effort includes seamless integration of enterprise systems, including enterprise resource planning (ERP), customer relationship management (CRM), learning management systems, and Microsoft-based platforms, into a cloud-native architecture that supports development, test, and production environments. This approach is streamlining data governance and accelerating the deployment of AI applications, while enabling cross-departmental upskilling and shared technical fluency across data, IT, security, and business teams.

The project lays the groundwork for self-service data exploration, AI-assisted querying, and advanced automation.

Challenges Addressed: Siloed Systems, Manual Reporting, and AI Limitations
Like many higher education institutions, the University of St. Thomas was constrained by fragmented infrastructure, redundant technologies, and heavy manual effort across its data environment:

• Siloed systems: Disconnected ed-tech solutions and point integrations created data inconsistencies and limited interoperability. 
• Manual business intelligence processes: Reporting required significant analyst effort, with turnaround times that couldn’t keep up with real-time needs. 
• Limited AI readiness: Without a unified data foundation, the institution struggled to operationalize AI and scale innovation. 

These limitations prompted a strategic shift toward a decoupled, cloud-native environment that could eliminate redundancy, reduce costs, and support both day-to-day operations and long-term transformation.

Results: Lower Costs, Greater Access, and AI-Ready Innovation
The impact of this transformation has been substantial and quantifiable:

• Cost avoidance: Eliminated the need for expensive integration add-ons, saving approximately $20,000 annually. 
• Analyst time savings: AI-enabled query layer projected to reclaim at least two hours per day per analyst, resulting in operational savings of $1,000 daily. 
• Scalable governance & AI: Secure, governed environments support efficient data sharing and AI experimentation across the university, completely future-ready. 
• Agility with Data: We are no longer tied to specifics of a system or platform and can create consistency needed for all strategic efforts, or future migrations. 

These outcomes not only reduce operational costs but also future-proof the university’s technology strategy, enabling greater data and AI responsiveness, collaboration, and innovation across departments.

“The University of St. Thomas excels at innovation by focusing on purpose-driven solutions that serve both our community and the common good. Our staff and faculty are committed to lifelong learning, a shared growth mindset with trust, and leading the education industry in applying data, AI, and technology to serve the whole person.” — Jena Zangs, Chief Data & Analytics Officer, University of St. Thomas

Setting a New Standard for Institutional Agility
With this initiative, the University of St. Thomas is not only preparing for the future of AI in education, it’s creating a model for how institutions of any size can overcome legacy constraints, unify their data landscape, and lead with purpose-driven innovation.